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\begin{abstract}
%% Text of abstract

This paper presents a face recognition system using a hybrid clustring algorithm that combines Particle Swarm Optimization and Nelder-Mead search method. The system starts by computing the eigenfaces for all faces in the database and then uses eigen weight vectors as feature vectors. Next, a particle swarm optimization algorithm is used to group inputs feature vectors into clusters. The Nelder-Mead search is integrated in the PSO clustring to efficenctly converge to a global optimum solution.  The paper uses two datasets to conduct experiments on the system.  The results shows that the system gives better result compared to K-means clustering
%The recongition problem is converted into a clustering problem where each person in the database is represented with a cluster. 
 %PSO algorithm was showed to   The PSO cshows that it successfully converge during the initial global search stages to around global optimum.Various experiements is conducted to proof the efficency of the system
%successfully converge during the initial stages of a global search, but around
%global optimum

%where the faces in the database are grouped into clusters using the proposed
% algorithm. When a new face is introduced to the system, the system tries to
% identify the cluster that the face belong to and recognize the face based on
% the cluster
%
\end{abstract}

%\textit{\textbf{Keywords:} Autonomous robot, Stochastic control, Kalman filter,
%Fuzzy logic, Neural Network, Adaptive navigation.}ir
